CT Radiomics Combined with Metabolic-Biomarkers Enables Early Recurrence Prediction in Hepatocellular Carcinoma
Bibliographic record
Abstract
Liying Ren,1,2,* Dongbo Chen,2,* Tingfeng Xu,3,* Rongyu Wei,1 Bigeng Zhao,1 Yuanping Zhou,4 Yong He,5 Minjun Liao,2 Hongsong Chen,2 Weijia Liao1 1Laboratory of Hepatobiliary and Pancreatic Surgery, the First Affiliated Hospital of Guilin Medical University, Guilin, Guangxi, People’s Republic of China; 2Peking University Hepatology Institute, Peking University People’s Hospital, Beijing Key Laboratory of Hepatitis C and Immunotherapy for Liver Disease, Beijing, People’s Republic of China; 3Affiliation Department of Hepatobiliary Surgery, the First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People’s Republic of China; 4Guangdong Provincial Key Laboratory of Gastroenterology, Department of Gastroenterology and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People’s Republic of China; 5Department of Radiology, the Second Affiliated Hospital of Guilin Medical University, Guilin, Guangxi, People’s Republic of China*These authors contributed equally to this workCorrespondence: Weijia Liao, Laboratory of Hepatobiliary and Pancreatic Surgery, the First Affiliated Hospital of Guilin Medical University, No. 15, Lequn Road, Xiufeng District, Guilin, Guangxi, 541001, People’s Republic of China, Email liaoweijia288@163.com Hongsong Chen, Peking University Hepatology Institute, Peking University People’s Hospital, No. 11 Xizhimen South Street, Beijing, 100044, People’s Republic of China, Email chenhongsong2999@163.comBackground: The prognosis of early recurrence of hepatocellular carcinoma (HCC) remains poor. This study aimed to develop and validate a radiomics model and determine potential biomarkers involved in biological pathways for early recurrence of HCC.Methods: A total of 271 HCC patients from the First Affiliated Hospital of Guilin Medical University were enrolled as the training cohort. Recurrence related radiomics features were determined by analyzing contrast-enhanced CT images, which were used for the construction of Rad-score. For external validation, we utilized both imaging and transcriptome data from 34 HCC patients in TCGA database. The identified radiomics-related genes were further validated using two independent datasets (OEP000321 and GSE14520) and immunohistochemical analysis of EEF1E1 in 38 HCC tissue samples from training cohort.Results: Rad-scores based on six radiomics features showed predictive value for early HCC recurrence in both cohorts (A465, A466, A839, V105, V250, V291). Relevant radiomics features are associated with metabolism, proliferation, and immune pathways. The most relevant recurrence-related radiomics gene module was determined via weighted correlation network analysis (WGCNA), which contained LRP12, GPD1L, GARS, EEF1E1, and DGKG. The model based on these genes could efficiently predict early HCC recurrence and was verified in the OEP000321 and GSE14520 datasets. Moreover, EEF1E1 was significantly associated with the Rad-score, illustrated prognostic value at the transcription level, and validated by immunohistochemical staining at the protein level.Conclusion: Rad-score and radiomics gene signatures from enhanced CT effectively predicted early recurrence in HCC, while EEF1E1 might serve as an efficient biomarker for early recurrence prediction for hepatocellular carcinoma. Keywords: contrast-enhanced CT, hepatocellular carcinoma, radiomics, early recurrence, EEF1E1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".